Ymcki Q4_XS 48B Parameters

VRAM Requirements for
Kimi-Linear-48B-A3B-Instruct-GGUF Q4_XS

To run Kimi-Linear-48B-A3B-Instruct-GGUF locally at Q4_XS quantization, you need at minimum 29.79 GB of GPU VRAM.

29.79 GB
Required VRAM
27 GB
File Size
8K tokens
Context Window
48B
Parameters
Estimated VRAM Required
29.79
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (Q4_XS) 27 GB
Context Overhead (8,192 tokens × 48B × 2 ÷ 1M) 0.786 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 29.79 GB

Try a Different Quantization

Use the interactive calculator to compare Kimi-Linear-48B-A3B-Instruct-GGUF across all available formats.

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Kimi-Linear-48B-A3B-Instruct-GGUF — Other Quantizations

Q2_K 15 GB file
17.79 GB
Q3_M 21 GB file
23.79 GB
Q4_K_M 27 GB file
29.79 GB
Q4_XS Current
29.79 GB
Q5_K_M 33 GB file
35.79 GB
FP4 48 GB file
50.79 GB
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Frequently Asked Questions

Can I run Kimi-Linear-48B-A3B-Instruct-GGUF Q4_XS on a consumer GPU?
Running Kimi-Linear-48B-A3B-Instruct-GGUF Q4_XS locally requires 29.79 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Kimi-Linear-48B-A3B-Instruct-GGUF?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is Q4_XS quality good enough for production?
Q4_K_M is an excellent balance of quality and performance. Perplexity tests show minimal degradation (< 2%) vs FP16 for most models. Suitable for most production applications.